Extending Contexts with Ontologies for Multidimensional Data Quality\n Assessment
Bibliographic record
Abstract
Data quality and data cleaning are context dependent activities. Starting\nfrom this observation, in previous work a context model for the assessment of\nthe quality of a database instance was proposed. In that framework, the context\ntakes the form of a possibly virtual database or data integration system into\nwhich a database instance under quality assessment is mapped, for additional\nanalysis and processing, enabling quality assessment. In this work we extend\ncontexts with dimensions, and by doing so, we make possible a multidimensional\nassessment of data quality assessment. Multidimensional contexts are\nrepresented as ontologies written in Datalog+-. We use this language for\nrepresenting dimensional constraints, and dimensional rules, and also for doing\nquery answering based on dimensional navigation, which becomes an important\nauxiliary activity in the assessment of data. We show ideas and mechanisms by\nmeans of examples.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".